Project Summary Studies from hospital-based care suggest that hundreds of thousands of Americans die each year due to adverse events occurring in the delivery of care, making it one of the leading causes of preventable death in the United States. Emergency Medical Services (EMS) play a critical role in health and outcomes of Americans, providing the first response of the health care system for emergencies, natural disasters and traumas. While quality and safety issues have been examined and are being addressed in the hospital setting, they have largely not been evaluated in the prehospital EMS setting. EMS providers work in teams and their teamwork is critical to patient safety. The nature of teams ? number of providers, training and skills, task assignments, and interactions between team members ? is complex and dynamic. Studies that investigate clinical teamwork traditionally use single-point estimates and summative values to measure team performance. This approach does not describe how, why, or when variations occur, each of which could inform patient safety, determine inflection points for evidence-base care, and contribute to the development of interventions that improve teamwork and the quality of care. This proposal seeks to remedy this knowledge gap by adapting sequence alignment (SA) to systematically and holistically model clinical team processes. SA is a visual analytical technique that is used to align common elements between sequences and measure similarity, such nucleic acids in DNA or amino acids in proteins. This technique has also been used to align tasks performed by individuals in human activity. Tasks are different from amino and nucleic acids in that they span some duration of time and can be characterized by different attributes. For example, Sue performed resuscitation for five minutes in the ambulance followed by a pulse check for ten seconds. In teams, members can perform tasks simultaneously. This work plans to identify patterns of activity that influence performance in EMS teams by: 1) developing a temporal multi-dimensional analysis technique, 2) using that technique to identify patterns that distinguish high- and low-performing teams in simulation, and 3) determining if those patterns persist across different types of clinical cases. This research advances AHRQ's priorities of improving health care quality and promoting patient safety. New technologies and sophisticated analytic techniques offer unprecedented opportunities to understand and improve care delivery and patient safety. This F32 postdoctoral fellowship research and training plan will prepare me to advance as an independent investigator and to launch a program of research involving the application of computer technologies to model and interpret clinical team behaviors and target interventions to improve team performance, quality of care, and patient safety.